Business Objects Reloads Mid-Market Product Line

Business Objects Reloads Mid-Market Product Line

7/23/2008

By Stephen Swoyer

With new releases of two of its signal SME offerings on tap this week, Business Objects -- now an SAP AG company -- hopes to build on its success in the small to mid-size company market. The rules of the mid-market game are changing, but one thing hasn't changed: SME customers -- much like their larger enterprise brethren -- are still wild about reporting. It's the bedrock of BI, after all.

That's why Business Objects -- which spent more than $800 million five years ago to acquire one of the best-known reporting brands on the market -- thinks it has an ace up its sleeve. With the release this week of revamped versions of its Crystal Reports Server and Business Objects Edge products, officials are pulling out all of the stops to burnish the company's mid-market credentials.

Prior to SAP's acquisition, Business Objects fired off several successful mid-market offerings. It released Crystal Reports Server (a version of Crystal Reports that was packaged, tweaked, and configured for SMEs), Business Objects Edge bundle (a mid-market-ready version of its Business Objects enterprise software stack), and launched several initiatives (including its Business Objects Rapid Marts -- basically, out-of-the-box data marts that provide turnkey integration with popular data sources such as SAP). James Thomas, vice-president of business intelligence tools -- and long-time Crystal Reports point-person -- with Business Objects, claims that his company knows SME customers: seven-eighths of its customer base is technically part of the mid-market....... more look at ... Click right click new window

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Business Objects Reloads Mid-Market Product Line

Business Objects Reloads Mid-Market Product Line

7/23/2008

By Stephen Swoyer

With new releases of two of its signal SME offerings on tap this week, Business Objects -- now an SAP AG company -- hopes to build on its success in the small to mid-size company market. The rules of the mid-market game are changing, but one thing hasn't changed: SME customers -- much like their larger enterprise brethren -- are still wild about reporting. It's the bedrock of BI, after all.

That's why Business Objects -- which spent more than $800 million five years ago to acquire one of the best-known reporting brands on the market -- thinks it has an ace up its sleeve. With the release this week of revamped versions of its Crystal Reports Server and Business Objects Edge products, officials are pulling out all of the stops to burnish the company's mid-market credentials.

Prior to SAP's acquisition, Business Objects fired off several successful mid-market offerings. It released Crystal Reports Server (a version of Crystal Reports that was packaged, tweaked, and configured for SMEs), Business Objects Edge bundle (a mid-market-ready version of its Business Objects enterprise software stack), and launched several initiatives (including its Business Objects Rapid Marts -- basically, out-of-the-box data marts that provide turnkey integration with popular data sources such as SAP). James Thomas, vice-president of business intelligence tools -- and long-time Crystal Reports point-person -- with Business Objects, claims that his company knows SME customers: seven-eighths of its customer base is technically part of the mid-market....... more look at ... Click right click new window

Read more »

Decode Function

n Oracle/PLSQL, the decode function has the functionality of an IF-THEN-ELSE statement.

The syntax for the decode function is:

decode( expression , search , result [, search , result]... [, default] )

expression is the value to compare.

search is the value that is compared against expression.

result is the value returned, if expression is equal to search.

default is optional. If no matches are found, the decode will return default. If default is omitted, then the decode statement will return null (if no matches are found).

For example:

You could use the decode function in an SQL statement as follows:

SELECT supplier_name,
decode(supplier_id,	10000,	'IBM',
	10001,	'Microsoft',
	10002,	'Hewlett Packard',
		'Gateway') result
FROM suppliers;

The above decode statement is equivalent to the following IF-THEN-ELSE statement:

IF supplier_id = 10000 THEN
result := 'IBM';

ELSIF supplier_id = 10001 THEN
result := 'Microsoft';

ELSIF supplier_id = 10002 THEN
result := 'Hewlett Packard';

ELSE
result := 'Gateway';

END IF;

The decode function will compare each supplier_id value, one by one.

second Example:

Using date compare two dates

Using the decode function to compare two dates (D1 and D2), where if D1 > D2, the decode function should return D2. Otherwise, the decode function should return D1.

decode((D1 - D2) - abs(D1 - D2), 0, D2, D1)

for more examples .............click to new window

Read more »

Decode Function

n Oracle/PLSQL, the decode function has the functionality of an IF-THEN-ELSE statement.

The syntax for the decode function is:

decode( expression , search , result [, search , result]... [, default] )

expression is the value to compare.

search is the value that is compared against expression.

result is the value returned, if expression is equal to search.

default is optional. If no matches are found, the decode will return default. If default is omitted, then the decode statement will return null (if no matches are found).

For example:

You could use the decode function in an SQL statement as follows:

SELECT supplier_name,
decode(supplier_id,	10000,	'IBM',
	10001,	'Microsoft',
	10002,	'Hewlett Packard',
		'Gateway') result
FROM suppliers;

The above decode statement is equivalent to the following IF-THEN-ELSE statement:

IF supplier_id = 10000 THEN
result := 'IBM';

ELSIF supplier_id = 10001 THEN
result := 'Microsoft';

ELSIF supplier_id = 10002 THEN
result := 'Hewlett Packard';

ELSE
result := 'Gateway';

END IF;

The decode function will compare each supplier_id value, one by one.

second Example:

Using date compare two dates

Using the decode function to compare two dates (D1 and D2), where if D1 > D2, the decode function should return D2. Otherwise, the decode function should return D1.

decode((D1 - D2) - abs(D1 - D2), 0, D2, D1)

for more examples .............click to new window

Read more »

Differences between OLTP and OLAP databases.

Differences between OLTP and OLAP databases.

Relational Database (OLTP)
Data is atomized
Data is current
Processes many records at a time
Process oriented
Designed for highly structured repetitive processing

Dimensional Database (OLAP)

Data is summarized
Data is historical
Processes one record at a time
Subject oriented
Designed for highly unstructured analytical processing

Read more »

Differences between OLTP and OLAP databases.

Differences between OLTP and OLAP databases.

Relational Database (OLTP)
Data is atomized
Data is current
Processes many records at a time
Process oriented
Designed for highly structured repetitive processing

Dimensional Database (OLAP)

Data is summarized
Data is historical
Processes one record at a time
Subject oriented
Designed for highly unstructured analytical processing

Read more »

Operational data store (ODS)

Operational data store

A subject-oriented system that is optimized for looking up one or two records at a time for decision making. An operational data store is a hybrid form of data warehouse that contains timely, current, integrated information. The data typically is of a higher level granularity than the transaction. You can use an operational data store for clerical, day-to-day decision making. This data can serve as the common source of data for data warehouses.

Read more »

Operational data store (ODS)

Operational data store

A subject-oriented system that is optimized for looking up one or two records at a time for decision making. An operational data store is a hybrid form of data warehouse that contains timely, current, integrated information. The data typically is of a higher level granularity than the transaction. You can use an operational data store for clerical, day-to-day decision making. This data can serve as the common source of data for data warehouses.

Read more »

Data Mart

What is Data mart?

A subset of data warehouse that is stored in a smaller database and that is oriented toward a specific purpose or data subject rather than for enterprise-wide strategic planning. A data mart can contain operational data, summarized data, spatial data, or metadata. Typically, you use a dimensional data model to build a data mart.

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Data Mart

What is Data mart?

A subset of data warehouse that is stored in a smaller database and that is oriented toward a specific purpose or data subject rather than for enterprise-wide strategic planning. A data mart can contain operational data, summarized data, spatial data, or metadata. Typically, you use a dimensional data model to build a data mart.

Read more »

The Star Schema

A good dimensional data warehouse has a simple database structure. Technically, a simple structure means faster queries. In a dimensional data warehouse, the implementation relational database has two types of tables, the fact table and the dimension table. The fact table consists of the facts (or measures) of the business. Dimension tables contain descriptions for querying the database.

The tables in a data warehouse are related such that the schema looks like a star, hence the term star schema


A star consists of a fact table surrounded by two or more dimension tables. A one-star structure has one fact table only. A multi-star structure has multiple fact tables, one for each star

dimension tables may be shared among multiple fact tables.

In Data warehouse the star structure, the snowflake structure could also be used However, it is more difficult to model than the star structure. As well, the snowflake structure is not that easy to understand and implement and the performance of its queries is slower than that of the star structure. These drawbacks make the snowflake structure unsuitable for dimensional data warehousing.

The Dimension, Fact table used term to suffix the table _dim, _fact.

Every Dimension table has one or more primary key which is reference to fact table. so based on this we can build query to retrieve the date what we required.

Above figure..

The customer_dim (customer dimensional table) is reference the Sales_order_fact table so that we can trace customer information. By using the Product_id in sales_order_fact table, we can retrieve the product information in production_dim table. By joining the sales_order_fact table and the date_dim table on the date_id column, you’ll get the order date. And by joining the fact table and the order_dim table on the order_id column, you obtain the order number.

Read more »

The Star Schema

A good dimensional data warehouse has a simple database structure. Technically, a simple structure means faster queries. In a dimensional data warehouse, the implementation relational database has two types of tables, the fact table and the dimension table. The fact table consists of the facts (or measures) of the business. Dimension tables contain descriptions for querying the database.

The tables in a data warehouse are related such that the schema looks like a star, hence the term star schema


A star consists of a fact table surrounded by two or more dimension tables. A one-star structure has one fact table only. A multi-star structure has multiple fact tables, one for each star

dimension tables may be shared among multiple fact tables.

In Data warehouse the star structure, the snowflake structure could also be used However, it is more difficult to model than the star structure. As well, the snowflake structure is not that easy to understand and implement and the performance of its queries is slower than that of the star structure. These drawbacks make the snowflake structure unsuitable for dimensional data warehousing.

The Dimension, Fact table used term to suffix the table _dim, _fact.

Every Dimension table has one or more primary key which is reference to fact table. so based on this we can build query to retrieve the date what we required.

Above figure..

The customer_dim (customer dimensional table) is reference the Sales_order_fact table so that we can trace customer information. By using the Product_id in sales_order_fact table, we can retrieve the product information in production_dim table. By joining the sales_order_fact table and the date_dim table on the date_id column, you’ll get the order date. And by joining the fact table and the order_dim table on the order_id column, you obtain the order number.

Read more »

Resolving Loops in a Universe

Define what a Loop is
Detect and recognize loops in Universe
Using Aliases and Contexts to resolve the loops
Identify when in the design process you resolve loops
Be able to use shortcut joins and know when to use them as alternative for resolving loops.
Be able to resolve recursive table structure loops.

…
A loop exists when the joins between tables form a continuous path

Three automated routines can be used to resolve most loop situations in the universe structure window.

1. Must be first Cardinality Detection
2. Alias Detection routine
3. Context Detection routine.

Click the Detect cardinalities or you can set manually also.

What is an Alias?
• An Alias is an exact duplicate of the original table with a new name. The data in the table is exactly the same.

• The Alias is used only to resolve the loop in the structure of the universe. There is no impact on the schema of the database
– Easy to define
– Easy to maintain
– Easy to use

• A loop with a single lookup table should be resolved by an alias
• A lookup table can be identified by its cardinality
• A lookup table only has the ‘one’ end of joins attached to it










Designer routines detect loops and candidates for aliases.


Break the loop by creating an alias of the lookup table for each side of the loop.
Some DBs require a separate alias for both sides of the loop.

After that you can detect loop once again,
Alias detection routine
You can use automatic loop detection also,

Click the Detect loops button, it will be identifies loops are exist or not
You can get the message in Loop detect window and it suggest to candidate contexts or aliases

If it is structure multi star situation it not possible using alias.









Normalized dimensions are not amenable to loop resolution using Aliases

• If you try to resolve the loop using Aliases:











And also we get duplicate of objects














This will be effective sales universe Rentals universe

In this case we can use to resolve with contexts.











after result look like this way.

Detecting and create context.


Double click the context in the list mode window

- give it context name
- if want write any description to update description this will be user

Read more »

Resolving Loops in a Universe

Define what a Loop is
Detect and recognize loops in Universe
Using Aliases and Contexts to resolve the loops
Identify when in the design process you resolve loops
Be able to use shortcut joins and know when to use them as alternative for resolving loops.
Be able to resolve recursive table structure loops.

…
A loop exists when the joins between tables form a continuous path

Three automated routines can be used to resolve most loop situations in the universe structure window.

1. Must be first Cardinality Detection
2. Alias Detection routine
3. Context Detection routine.

Click the Detect cardinalities or you can set manually also.

What is an Alias?
• An Alias is an exact duplicate of the original table with a new name. The data in the table is exactly the same.

• The Alias is used only to resolve the loop in the structure of the universe. There is no impact on the schema of the database
– Easy to define
– Easy to maintain
– Easy to use

• A loop with a single lookup table should be resolved by an alias
• A lookup table can be identified by its cardinality
• A lookup table only has the ‘one’ end of joins attached to it










Designer routines detect loops and candidates for aliases.


Break the loop by creating an alias of the lookup table for each side of the loop.
Some DBs require a separate alias for both sides of the loop.

After that you can detect loop once again,
Alias detection routine
You can use automatic loop detection also,

Click the Detect loops button, it will be identifies loops are exist or not
You can get the message in Loop detect window and it suggest to candidate contexts or aliases

If it is structure multi star situation it not possible using alias.









Normalized dimensions are not amenable to loop resolution using Aliases

• If you try to resolve the loop using Aliases:











And also we get duplicate of objects














This will be effective sales universe Rentals universe

In this case we can use to resolve with contexts.











after result look like this way.

Detecting and create context.


Double click the context in the list mode window

- give it context name
- if want write any description to update description this will be user

Read more »

Data warehouse Concepts - Day 2 i

What is datawarehouse?

A data warehouse is built on relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine

Characteristics of a data warehouse

Subject Oriented
Integrated
Nonvolatile
Time Variant

Different Types of Data

Metadata
Dimension data
Fact Data
Aggregated Data

Data warehouse Architectures



Dimension Modeling


Identify a Business Process
Identify Grain
Identify elements which describe the process (Dimensions)
Identify elements which measure process(facts)10

Read more »

Data warehouse Concepts - Day 2 i

What is datawarehouse?

A data warehouse is built on relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine

Characteristics of a data warehouse

Subject Oriented
Integrated
Nonvolatile
Time Variant

Different Types of Data

Metadata
Dimension data
Fact Data
Aggregated Data

Data warehouse Architectures



Dimension Modeling


Identify a Business Process
Identify Grain
Identify elements which describe the process (Dimensions)
Identify elements which measure process(facts)10

Read more »

Data warehouse Concepts - Day 2

dimension table:
A dimensional table is a collection of hierarchies and categories along which the user can drill down and drill up. it contains only the textual attributes.

LookUp table

A LookUp table is the one which is used when updating a warehouse. When the lookup is placed on the target table (fact table / warehouse) based upon the primary key of the target, it just updates the table by allowing only new records or updated records based on the lookup condition.

Aggregate table

Aggregate table contains the summary of existing warehouse data which is grouped to certain levels of dimensions.Retrieving the required data from the actual table, which have millions of records will take more time and also affects the server performance.To avoid this we can aggregate the table to certain required level and can use it.This tables reduces the load in the database server and increases the performance of the query and can retrieve the result very fastly

What is ODS?

1. ODS means Operational Data Store.
2. A collection of operation or bases data that is extracted from operation databases and standardized, cleansed, consolidated, transformed, and loaded into an enterprise data architecture. An ODS is used to support data mining of operational data, or as the store for base data that is summarized for a data warehouse. The ODS may also be used to audit the data warehouse to assure summarized and derived data is calculated properly. The ODS may further become the enterprise shared operational database, allowing operational systems that are being reengineered to use the ODS as there operation databases.

Read more »

Data warehouse Concepts - Day 2

dimension table:
A dimensional table is a collection of hierarchies and categories along which the user can drill down and drill up. it contains only the textual attributes.

LookUp table

A LookUp table is the one which is used when updating a warehouse. When the lookup is placed on the target table (fact table / warehouse) based upon the primary key of the target, it just updates the table by allowing only new records or updated records based on the lookup condition.

Aggregate table

Aggregate table contains the summary of existing warehouse data which is grouped to certain levels of dimensions.Retrieving the required data from the actual table, which have millions of records will take more time and also affects the server performance.To avoid this we can aggregate the table to certain required level and can use it.This tables reduces the load in the database server and increases the performance of the query and can retrieve the result very fastly

What is ODS?

1. ODS means Operational Data Store.
2. A collection of operation or bases data that is extracted from operation databases and standardized, cleansed, consolidated, transformed, and loaded into an enterprise data architecture. An ODS is used to support data mining of operational data, or as the store for base data that is summarized for a data warehouse. The ODS may also be used to audit the data warehouse to assure summarized and derived data is calculated properly. The ODS may further become the enterprise shared operational database, allowing operational systems that are being reengineered to use the ODS as there operation databases.

Read more »

Data warehouse Concepts - Day 1

Data warehouse :
A Data warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated. This makes it much easier and more efficient to run queries over data that originally came from different sources". Another definition for data warehouse is: " A data warehouse is a logical collection of information gathered from many different operational databases used to create business intelligence that supports business analysis activities and decision-making tasks, primarily, a record of an enterprise's past transactional and operational information, stored in a database designed to favour efficient data analysis and reporting (especially OLAP)". Generally, data warehousing is not meant for current "live" data, although 'virtual' or 'point-to-point' data warehouses can access operational data. A 'real' data warehouse is generally preferred to a virtual DW because stored data has been validated and is set up to provide reliable results to common types of queries used in a business

Data Warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated....This makes it much easier and more efficient to run queries over data that originally came from different sources.
Typical relational databases are designed for on-line transactional processing (OLTP) and do not meet the requirements for effective on-line analytical processing (OLAP). As a result, data warehouses are designed differently than traditional relational databases


Data Warehousing is concerned with the activities involved with creating a DataRepository where you store all the facts you know about some large enterprise or activity, and then analyse the data in different ways,to look for patterns on the data. Typically this is done by large retail organisations, trying to identify patterns in buying behaviour so they can sell more stuff to the poor unsuspecting customer.

Read more »

Data warehouse Concepts - Day 1

Data warehouse :
A Data warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated. This makes it much easier and more efficient to run queries over data that originally came from different sources". Another definition for data warehouse is: " A data warehouse is a logical collection of information gathered from many different operational databases used to create business intelligence that supports business analysis activities and decision-making tasks, primarily, a record of an enterprise's past transactional and operational information, stored in a database designed to favour efficient data analysis and reporting (especially OLAP)". Generally, data warehousing is not meant for current "live" data, although 'virtual' or 'point-to-point' data warehouses can access operational data. A 'real' data warehouse is generally preferred to a virtual DW because stored data has been validated and is set up to provide reliable results to common types of queries used in a business

Data Warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated....This makes it much easier and more efficient to run queries over data that originally came from different sources.
Typical relational databases are designed for on-line transactional processing (OLTP) and do not meet the requirements for effective on-line analytical processing (OLAP). As a result, data warehouses are designed differently than traditional relational databases


Data Warehousing is concerned with the activities involved with creating a DataRepository where you store all the facts you know about some large enterprise or activity, and then analyse the data in different ways,to look for patterns on the data. Typically this is done by large retail organisations, trying to identify patterns in buying behaviour so they can sell more stuff to the poor unsuspecting customer.

Read more »